Tree models can be generalized to tree ensembles, using ensemble learning techniques, such as
bagging and boosting. Notably, the random forest method generates multiple decision trees according to a variant of bagging
[1]}, [2]}. The output representation is a multi-set
\(F = \lbrace T_1,\cdots ,T_m\rbrace \) 
of decision trees, and the corresponding concept is given by:
\(F(\mathbf {x}) ={\left\lbrace \begin{array}{ll}1 & \mbox{ if } \sum _{i=1}^m T_i(\mathbf {x}) > \frac{m}{2} \\0 & \mbox{ otherwise.}\end{array}\right.}\) 
